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Advances in Process Optimization: A Comprehensive Survey of Process Mining, Predictive Process Monitoring, and Process-Aware Recommender Systems

Asjad Khan, Aditya Ghose, Hoa Dam, Arsal Syed

TL;DR

This survey tackles the challenge of turning heterogeneous process execution data into actionable BPM insights by synthesizing three capabilities: mining process behaviour, predictive process monitoring, and prescriptive decision support. It conducts a systematic literature review, develops a taxonomy across data types, process perspectives, algorithms, and evaluations, and highlights data quality, privacy, knowledge integration, and explainability as key research directions. The work provides a comprehensive framework to guide researchers and practitioners in building AI-augmented, risk-aware BPM systems and underscores the need for benchmarks and standardized evaluation to advance the field. By connecting traditional process mining with predictive and prescriptive analytics, the paper emphasizes practical impacts on efficiency, compliance, and adaptive decision-making in diverse domains.

Abstract

Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization.

Advances in Process Optimization: A Comprehensive Survey of Process Mining, Predictive Process Monitoring, and Process-Aware Recommender Systems

TL;DR

This survey tackles the challenge of turning heterogeneous process execution data into actionable BPM insights by synthesizing three capabilities: mining process behaviour, predictive process monitoring, and prescriptive decision support. It conducts a systematic literature review, develops a taxonomy across data types, process perspectives, algorithms, and evaluations, and highlights data quality, privacy, knowledge integration, and explainability as key research directions. The work provides a comprehensive framework to guide researchers and practitioners in building AI-augmented, risk-aware BPM systems and underscores the need for benchmarks and standardized evaluation to advance the field. By connecting traditional process mining with predictive and prescriptive analytics, the paper emphasizes practical impacts on efficiency, compliance, and adaptive decision-making in diverse domains.

Abstract

Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization.
Paper Structure (30 sections, 2 figures)

This paper contains 30 sections, 2 figures.

Table of Contents

  1. Introduction
  2. PRELIMINARY CONCEPTS
  3. Research Methodology
  4. Research Questions
  5. Study Retrieval
  6. Study Selection
  7. Taxonomy
  8. Mining Process Behaviour
  9. Process Performance Mining
  10. Causal Process Mining
  11. Deviance Mining
  12. Decision Mining
  13. Resource Behaviour Analysis
  14. Distributed Privacy-Preserving Mining
  15. Knowledge-Centric Process Mining:
  16. ...and 15 more sections

Figures (2)

  • Figure 1: Publications dates of included literature
  • Figure 2: Proposed Taxonomy